@unpublished{67306,
  author       = {{Stenner, Jan and Harder, Hans and Peitz, Sebastian}},
  booktitle    = {{arXiv:2606.30238}},
  keywords     = {{own, own-preprint, erc}},
  title        = {{{Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection}}},
  year         = {{2026}},
}

@inproceedings{67299,
  author       = {{Harder, Hans and Vishwasrao, Abhijeet and Guastoni, Luca and Vinuesa, Ricardo and Peitz, Sebastian}},
  booktitle    = {{Proceedings of The 8th Annual Learning for Dynamics and Control Conference}},
  editor       = {{Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}},
  keywords     = {{own, own-conference, erc}},
  pages        = {{1601–1619}},
  publisher    = {{PMLR}},
  title        = {{{Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems}}},
  doi          = {{10.48550/arXiv.2511.04641}},
  volume       = {{331}},
  year         = {{2026}},
}

@article{46579,
  abstract     = {{The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear
dynamics from measurements. Until now, the situation where for large-scale systems, we (i) only have access to partial observations (i.e., measurements, as is very common for experimental data) or (ii) deliberately perform coarse
graining (for efficiency reasons) has not been treated to its full extent. In this paper, we address the pitfall associated with this situation, that the classical EDMD algorithm does not automatically provide a Koopman operator approximation for the underlying system if we do not carefully select the number of observables. Moreover, we show that symmetries in the system dynamics can be carried over to the Koopman operator, which allows us to massively increase the model efficiency. We also briefly draw a connection to domain decomposition techniques for partial differential equations and present numerical evidence using the Kuramoto--Sivashinsky equation.}},
  author       = {{Peitz, Sebastian and Harder, Hans and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}},
  journal      = {{Journal of Computational Dynamics}},
  pages        = {{305--324}},
  title        = {{{Equivariance and partial observations in Koopman operator theory for partial differential equations}}},
  doi          = {{10.3934/jcd.2024035}},
  volume       = {{12}},
  year         = {{2025}},
}

@inproceedings{53793,
  abstract     = {{We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance.}},
  author       = {{Harder, Hans and Peitz, Sebastian}},
  booktitle    = {{Systems theory in data and optimization}},
  editor       = {{Berberich, J. and Ianelli, A. and Allgöwer, F.}},
  isbn         = {{978-3-031-83191-1}},
  keywords     = {{extreme learning machines, partial differential equations, data-driven prediction, high-dimensional systems}},
  pages        = {{153--168}},
  publisher    = {{Springer}},
  title        = {{{Solving partial differential equations with equivariant extreme learning machines}}},
  doi          = {{10.1007/978-3-031-83191-1_10}},
  year         = {{2025}},
}

@article{67314,
  author       = {{Harder, Hans and Peitz, Sebastian and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}},
  journal      = {{Physica D: Nonlinear Phenomena}},
  keywords     = {{own, own-journal}},
  pages        = {{134725}},
  title        = {{{Group Convolutional Extended Dynamic Mode Decomposition}}},
  doi          = {{10.1016/j.physd.2025.134725}},
  volume       = {{480}},
  year         = {{2025}},
}

@unpublished{67318,
  author       = {{Fromme, Fynn and Allen-Blanchette, Christine and Harder, Hans and Peitz, Sebastian}},
  booktitle    = {{arXiv:2505.13569}},
  keywords     = {{own, own-preprint, erc}},
  title        = {{{Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders}}},
  year         = {{2025}},
}

@inproceedings{52758,
  author       = {{Harder, Hans and Peitz, Sebastian}},
  booktitle    = {{IEEE 63rd conference on decision and control (CDC)}},
  pages        = {{1935--1940}},
  publisher    = {{IEEE}},
  title        = {{{On the continuity and smoothness of the value function in reinforcement learning and optimal control}}},
  doi          = {{10.1109/CDC56724.2024.10886529}},
  year         = {{2024}},
}